Deficit versus social statistics: empirical evidence for the effectiveness of Benford’s law

Rauch, Bernhard; Göttsche, Max; Brähler, Gernot

doi:10.1080/13504851.2013.844319

Cited by 10 publications

(6 citation statements)

References 8 publications

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“…It has been used to audit industrial and financial data (Rauch, Göttsche, Brähler, & Kronfeld, 2014; Rauch, Göttsche, & El Mouaaouy, 2013), to gauge the scientific publication process (de Vries & Murk, 2013), to separate natural from computer-generated images (Tong, Yang, & Xie, 2013), or to detect hidden messages in images’ .jpeg files (Andriotis, Oikonomou, & Tryfonas, 2013). …”

Section: The Sensitivity and Specificity Of Benford Analysismentioning

confidence: 99%

“…These results support the so-called Benford analysis , which uses a measure of discrepancy from NBL to detect fraudulent or erroneous data ( Bolton & Hand, 2002 ; Kumar, 2013 ; Nigrini, 2012 ). It has been used to audit industrial and financial data ( Rauch, Göttsche, Brähler, & Kronfeld, 2014 ; Rauch, Göttsche, & El Mouaaouy, 2013 ), to gauge the scientific publication process ( de Vries & Murk, 2013 ), to separate natural from computer-generated images ( Tong, Yang, & Xie, 2013 ), or to detect hidden messages in images’ .jpeg files ( Andriotis, Oikonomou, & Tryfonas, 2013 ).…”

Section: The Sensitivity and Specificity Of Benford Analysismentioning

confidence: 99%

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Generalized Benford’s Law as a Lie Detector

Gauvrit¹,

Houillon²,

Delahaye³

2017

ACP

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The first significant (leftmost nonzero) digit of seemingly random numbers often appears to conform to a logarithmic distribution, with more 1s than 2s, more 2s than 3s, and so forth, a phenomenon known as Benford’s law. When humans try to produce random numbers, they often fail to conform to this distribution. This feature grounds the so-called Benford analysis, aiming at detecting fabricated data. A generalized Benford’s law (GBL), extending the classical Benford’s law, has been defined recently. In two studies, we provide some empirical support for the generalized Benford analysis, broadening the classical Benford analysis. We also conclude that familiarity with the numerical domain involved as well as cognitive effort only have a mild effect on the method’s accuracy and can hardly explain the positive results provided here.

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Section: The Sensitivity and Specificity Of Benford Analysismentioning

confidence: 99%

Section: The Sensitivity and Specificity Of Benford Analysismentioning

confidence: 99%

Generalized Benford’s Law as a Lie Detector

Gauvrit¹,

Houillon²,

Delahaye³

2017

ACP

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“…Consequently, the scope for the application of Benford’s law was initially limited to the settings prosaically associated with strategic misreporting: tax filings [ 32 ], political campaign financing and election results [ 33 , 34 ], and survey results [ 35 , 36 ]. Stimulated by the encouraging results of these early exploratory works, a new wave of explanatory works used Benford’s law to test theories on cheating in settings less often associated with strategic misreporting: balances of payments [ 8 ], budget deficit statistics [ 15 , 37 ], academic publications [ 38 , 39 ] and macroeconomic statistics [ 16 , 40 , 41 ].…”

Section: Literature Overviewmentioning

confidence: 99%

“…e . while the quality of the social security statistics appears to be higher , there is a widespread tendency to report incorrect deficit data” [ 37 ].…”

Section: Literature Overviewmentioning

confidence: 99%

Do Countries Consistently Engage in Misinforming the International Community about Their Efforts to Combat Money Laundering? Evidence Using Benford’s Law

Deleanu

2017

PLoS ONE

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Indicators of compliance and efficiency in combatting money laundering, collected by EUROSTAT, are plagued with shortcomings. In this paper, I have carried out a forensic analysis on a 2003–2010 dataset of indicators of compliance and efficiency in combatting money laundering, that European Union member states self-reported to EUROSTAT, and on the basis of which, their efforts were evaluated. I used Benford’s law to detect any anomalous statistical patterns and found that statistical anomalies were also consistent with strategic manipulation. According to Benford’s law, if we pick a random sample of numbers representing natural processes, and look at the distribution of the first digits of these numbers, we see that, contrary to popular belief, digit 1 occurs most often, then digit 2, and so on, with digit 9 occurring in less than 5% of the sample. Without prior knowledge of Benford’s law, since people are not intuitively good at creating truly random numbers, deviations thereof can capture strategic alterations. In order to eliminate other sources of deviation, I have compared deviations in situations where incentives and opportunities for manipulation existed and in situations where they did not. While my results are not a conclusive proof of strategic manipulation, they signal that countries that faced incentives and opportunities to misinform the international community about their efforts to combat money laundering may have manipulated these indicators. Finally, my analysis points to the high potential for disruption that the manipulation of national statistics has, and calls for the acknowledgment that strategic manipulation can be an unintended consequence of the international community’s pressure on countries to put combatting money laundering on the top of their national agenda.

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“…Benford's law has been proven as an efficient tool in digital analysis technology [5,6]. Namely, the first-digit distribution (FDD) in accounting data of companies and unmanipulated macroeconomic data are expected to follow Benford's law [7][8][9]. First, Jolion [10] investigated Benford's law in the context of digital images.…”

Section: Introductionmentioning

confidence: 99%

No-Reference Video Quality Assessment Based on Benford’s Law and Perceptual Features

Varga

2021

Electronics

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No-reference video quality assessment (NR-VQA) has piqued the scientific community’s interest throughout the last few decades, owing to its importance in human-centered interfaces. The goal of NR-VQA is to predict the perceptual quality of digital videos without any information about their distortion-free counterparts. Over the past few decades, NR-VQA has become a very popular research topic due to the spread of multimedia content and video databases. For successful video quality evaluation, creating an effective video representation from the original video is a crucial step. In this paper, we propose a powerful feature vector for NR-VQA inspired by Benford’s law. Specifically, it is demonstrated that first-digit distributions extracted from different transform domains of the video volume data are quality-aware features and can be effectively mapped onto perceptual quality scores. Extensive experiments were carried out on two large, authentically distorted VQA benchmark databases.

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Deficit versus social statistics: empirical evidence for the effectiveness of Benford’s law

Cited by 10 publications

References 8 publications

Generalized Benford’s Law as a Lie Detector

Generalized Benford’s Law as a Lie Detector

Do Countries Consistently Engage in Misinforming the International Community about Their Efforts to Combat Money Laundering? Evidence Using Benford’s Law

No-Reference Video Quality Assessment Based on Benford’s Law and Perceptual Features

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